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"}}},{"cell_type":"markdown","source":"credit to Mrigendra Agrawal I am using his notebook and first predicting a single target and using it as a feature for predicting other targets\nhttps://www.kaggle.com/mrigendraagrawal/fork-of-1-35-lightgbm-ann-2505f2","metadata":{"_uuid":"16e1216b-c7cb-4e1d-b50a-a47d6a1fd7e4","_cell_guid":"86f40eb0-e71b-4808-ad96-aed6933284cc","papermill":{"duration":0.019681,"end_time":"2021-06-28T10:01:24.573551","exception":false,"start_time":"2021-06-28T10:01:24.55387","status":"completed"},"tags":[],"trusted":true}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"### 👷勉強のためコードを分解して理解しています。新しい知見を取り入れたい。このコードは元のやつから自分なりに少し修正しています<br>\n### もう一つのnotebookは、０から自力で作る方法で、二段構えでやっています。\n\n### このコンペは未来予測なのでいいスコアとっているから勝てるとかそういうものではないやつです。\n### もう１つのnotebookは現在47位になっていますが、あてになりません。","metadata":{"_uuid":"11452283-bc34-4eea-91ab-9cb0a321b527","_cell_guid":"60559659-7020-4de2-97e2-6cb0ea7a2aa5","trusted":true}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"#%%capture internet-onへ\n'''\n!pip install pandarallel #計算処理を並列化するPandaralellです。\n#　少し早くなる程度　https://www.salesanalytics.co.jp/datascience/datascience020/\n\nimport gc\n\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\nfrom pandarallel import pandarallel\npandarallel.initialize()\n\nBASE_DIR = Path('../input/mlb-player-digital-engagement-forecasting')\ntrain = pd.read_csv(BASE_DIR / 'train.csv')\n\nnull = np.nan\ntrue = True\nfalse = False\n\nfor col in train.columns:\n\n    if col == 'date': continue\n\n    _index = train[col].notnull()\n    train.loc[_index, col] = train.loc[_index, col].parallel_apply(lambda x: eval(x))\n\n    outputs = []\n    for index, date, record in train.loc[_index, ['date', col]].itertuples():\n        _df = pd.DataFrame(record)\n        _df['index'] = index\n        _df['date'] = date\n        outputs.append(_df)\n\n    outputs = pd.concat(outputs).reset_index(drop=True)\n\n    outputs.to_csv(f'{col}_train.csv', index=False) #=>>>>　これ？カラム分できるじゃん\n    outputs.to_pickle(f'{col}_train.pkl') \n\n    del outputs\n    del train[col]\n    gc.collect()\n    \n    ##　停止\n    break\n'''","metadata":{"_uuid":"067015aa-ba14-4e8d-82f7-4fc5cc52ef73","_cell_guid":"4e798455-3b1c-4c3a-b6d6-d660225b41a6","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":0.039892,"end_time":"2021-06-28T10:01:24.704449","exception":false,"start_time":"2021-06-28T10:01:24.664557","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-07-12T13:09:44.017827Z","iopub.execute_input":"2021-07-12T13:09:44.018527Z","iopub.status.idle":"2021-07-12T13:09:44.033999Z","shell.execute_reply.started":"2021-07-12T13:09:44.01843Z","shell.execute_reply":"2021-07-12T13:09:44.03296Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 🧍‍♂️事前処理分解開始。","metadata":{"_uuid":"47f704c9-7096-4105-aa13-fbd22032286c","_cell_guid":"3c39e735-645b-4c25-9a80-11e87708e65f","trusted":true}},{"cell_type":"markdown","source":"メモリ不足になるため確認後、コメントアウトしています。","metadata":{}},{"cell_type":"code","source":"'''\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\nnull = np.nan\ntrue = True\nfalse = False\n\nBASE_DIR = Path('../input/mlb-player-digital-engagement-forecasting')\ntrain = pd.read_csv(BASE_DIR / 'train.csv')\ntrain.head(5)\n'''","metadata":{"_uuid":"1449296d-e83c-4c5e-90ae-9b02d3e83835","_cell_guid":"e025178b-00e2-47e8-84c0-55b5cda0b537","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.085464Z","iopub.execute_input":"2021-07-12T13:09:44.085903Z","iopub.status.idle":"2021-07-12T13:09:44.092381Z","shell.execute_reply.started":"2021-07-12T13:09:44.085864Z","shell.execute_reply":"2021-07-12T13:09:44.09111Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#train.columns","metadata":{"_uuid":"76f87f1a-0626-4a5f-8bbc-3a6e94c24209","_cell_guid":"aae83836-55c3-4890-b3f9-fb7bc1ad52d6","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.118246Z","iopub.execute_input":"2021-07-12T13:09:44.118636Z","iopub.status.idle":"2021-07-12T13:09:44.123147Z","shell.execute_reply.started":"2021-07-12T13:09:44.118604Z","shell.execute_reply":"2021-07-12T13:09:44.121999Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"  #  col = 'nextDayPlayerEngagement'\n  #  _index = train[col].notnull()\n  #  _index.head(5)","metadata":{"_uuid":"f7911a5c-269f-446d-a29a-07f8f7808a5a","_cell_guid":"ee40fbc3-f7ab-4b22-ae5e-1fb057c1d14c","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.141876Z","iopub.execute_input":"2021-07-12T13:09:44.142287Z","iopub.status.idle":"2021-07-12T13:09:44.147312Z","shell.execute_reply.started":"2021-07-12T13:09:44.142248Z","shell.execute_reply":"2021-07-12T13:09:44.146018Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#len(train.loc[_index, col][:1][0])","metadata":{"_uuid":"fad5c42c-3577-4676-8991-c500f6dfed1c","_cell_guid":"041bf8b8-9a66-498c-8527-622d0a4baef5","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.177603Z","iopub.execute_input":"2021-07-12T13:09:44.17802Z","iopub.status.idle":"2021-07-12T13:09:44.182397Z","shell.execute_reply.started":"2021-07-12T13:09:44.177984Z","shell.execute_reply":"2021-07-12T13:09:44.1815Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#train.loc[_index, col][:1][0][:500]","metadata":{"_uuid":"7871ff30-9ba3-4951-b19f-372587119f9e","_cell_guid":"df4f65a5-8c68-4ac9-a5f2-3af194739383","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.19645Z","iopub.execute_input":"2021-07-12T13:09:44.197138Z","iopub.status.idle":"2021-07-12T13:09:44.201036Z","shell.execute_reply.started":"2021-07-12T13:09:44.197098Z","shell.execute_reply":"2021-07-12T13:09:44.200107Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#train.loc[_index, col].apply(lambda x: eval(x)) #まったくなにをしているかわからず","metadata":{"_uuid":"72255088-1488-4453-90d8-f16dd713e51a","_cell_guid":"2fbadec7-cc37-4915-aca4-4e4b0fbef65b","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.209113Z","iopub.execute_input":"2021-07-12T13:09:44.209772Z","iopub.status.idle":"2021-07-12T13:09:44.217737Z","shell.execute_reply.started":"2021-07-12T13:09:44.209729Z","shell.execute_reply":"2021-07-12T13:09:44.21664Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#len(train.loc[_index, col][:1][0])","metadata":{"_uuid":"6334620c-af82-4be6-944b-b9f86308982e","_cell_guid":"2b130e74-fcf4-41e8-bccd-3e90d85abd0a","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.219682Z","iopub.execute_input":"2021-07-12T13:09:44.220052Z","iopub.status.idle":"2021-07-12T13:09:44.233587Z","shell.execute_reply.started":"2021-07-12T13:09:44.220015Z","shell.execute_reply":"2021-07-12T13:09:44.232336Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#train.loc[_index, col][:1][0][:500]","metadata":{"_uuid":"ef9f51af-58a6-405a-8c7a-ee507ce5471e","_cell_guid":"64a622ea-46e2-4524-80ed-0c4c1110c3e9","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.235568Z","iopub.execute_input":"2021-07-12T13:09:44.236274Z","iopub.status.idle":"2021-07-12T13:09:44.24923Z","shell.execute_reply.started":"2021-07-12T13:09:44.236192Z","shell.execute_reply":"2021-07-12T13:09:44.247607Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"   # outputs = []\n   # for index, date, record in train.loc[_index, ['date', col]].itertuples():\n   #     print(index,date,record[:100])\n   #     break","metadata":{"_uuid":"3984df0c-1c19-4235-802e-978521ec83c2","_cell_guid":"e4ec1f4a-907d-429f-b3cc-2339945360fe","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.25147Z","iopub.execute_input":"2021-07-12T13:09:44.252287Z","iopub.status.idle":"2021-07-12T13:09:44.2667Z","shell.execute_reply.started":"2021-07-12T13:09:44.252221Z","shell.execute_reply":"2021-07-12T13:09:44.265698Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## <font color=\"orange\">pandasファイルを１行づつ読みたい場合のやり方です</font>\n0から作るとこの問題にぶつかります。単純に読もうとすると、１列しか読めなかったり。そこでこのやり方を使うのです。<br>\n\n<font color=\"#FE2EF7\"><u>DataFrame.itertuples()メソッド</u></font><br>\n-pandas.DataFrameをfor文でループ処理（イテレーション）する場合、単純にそのままfor文で回すと列名が返ってくるだけなので、繰り返し処理のためのメソッドを使って列ごと・行ごと（一列ずつ・一行ずつ）の値を取得する。<br>\n-for row in df.itertuples():<br>\n\n<u><font color=\"#FE2EF7\"> DataFrame.iterrows()メソッド</font></u><br>\n-iterrows()メソッドを使うと、1行ずつ、インデックス名（行名）とその行のデータ（pandas.Series型）のタプル(index, Series)を取得できる。<br>\n-for index, row in df.iterrows():","metadata":{"_uuid":"9a79dfff-078c-4e19-84d8-b16c2bcb1418","_cell_guid":"ff769d8b-aabe-4b01-b226-d6f2b99dff0f","trusted":true}},{"cell_type":"code","source":"%%capture\n'''\nfor col in train.columns:\n\n    if col == 'date': continue\n\n    _index = train[col].notnull()\n    train.loc[_index, col] = train.loc[_index, col].apply(lambda x: eval(x))\n\n    outputs = []\n    for index, date, record in train.loc[_index, ['date', col]].itertuples():\n        _df = pd.DataFrame(record)\n        _df['index'] = index\n        _df['date'] = date\n        outputs.append(_df)\n        break\n    break\n    '''","metadata":{"_uuid":"7bb64b19-3ff7-4248-b445-272b2c39f1f8","_cell_guid":"a2565078-947c-4f0f-95a2-b59178b397d4","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.26857Z","iopub.execute_input":"2021-07-12T13:09:44.269234Z","iopub.status.idle":"2021-07-12T13:09:44.287623Z","shell.execute_reply.started":"2021-07-12T13:09:44.269163Z","shell.execute_reply":"2021-07-12T13:09:44.286466Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"%%captureといれておくと、コメント行を実行したときに余計なメッセージがでないのでよく使います。","metadata":{}},{"cell_type":"markdown","source":"データはtrain.csvの各カラムで巨大データが入っている<br>\n'nextDayPlayerEngagement'のカラムには目的関数が入っている。","metadata":{"_uuid":"5c05706a-02ea-45f8-b5db-542fa0a910ec","_cell_guid":"dda5a30c-03e3-44c9-955c-db37cf279a84","trusted":true}},{"cell_type":"code","source":"#_df.head(5)","metadata":{"_uuid":"71cf5ac5-21af-454b-a67e-d510f00fb021","_cell_guid":"d62b8047-1e4c-4837-9750-d95b037611cf","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.289596Z","iopub.execute_input":"2021-07-12T13:09:44.290338Z","iopub.status.idle":"2021-07-12T13:09:44.299366Z","shell.execute_reply.started":"2021-07-12T13:09:44.290284Z","shell.execute_reply":"2021-07-12T13:09:44.297996Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#col","metadata":{"_uuid":"9e88c40a-c21c-459b-8f79-b9d6d406a065","_cell_guid":"02691ec9-de58-4003-92e3-1f42bd5e04dd","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.300954Z","iopub.execute_input":"2021-07-12T13:09:44.301318Z","iopub.status.idle":"2021-07-12T13:09:44.314133Z","shell.execute_reply.started":"2021-07-12T13:09:44.301281Z","shell.execute_reply":"2021-07-12T13:09:44.312793Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"recordには、辞書形式でデータが格納されている。なのでPandasで取り込める。","metadata":{"_uuid":"fbbad004-a759-4502-ac98-1866414d24b2","_cell_guid":"9840c179-85c9-489d-a448-a03ef273d884","trusted":true}},{"cell_type":"code","source":"#record[0]","metadata":{"_uuid":"fb5aa5f8-a2ab-45d6-9a0b-35779ddae99c","_cell_guid":"33be3756-a713-4b2e-8280-4e075f262cef","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.315886Z","iopub.execute_input":"2021-07-12T13:09:44.316436Z","iopub.status.idle":"2021-07-12T13:09:44.326784Z","shell.execute_reply.started":"2021-07-12T13:09:44.31638Z","shell.execute_reply":"2021-07-12T13:09:44.325367Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"不要データの削除","metadata":{"_uuid":"4b2eb311-90de-4585-a7d2-1f1855852525","_cell_guid":"59d29ccb-fb35-4cd4-af01-f99f2352cc73","trusted":true}},{"cell_type":"code","source":"#del train\n#del outputs\n#del record\n#del _df\n#gc.collect()","metadata":{"_uuid":"d06ed49f-4bb0-449c-8516-3027418c5278","_cell_guid":"13456e39-ad06-4d9d-8b87-8e72e07d7b42","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:44.328943Z","iopub.execute_input":"2021-07-12T13:09:44.329546Z","iopub.status.idle":"2021-07-12T13:09:44.339513Z","shell.execute_reply.started":"2021-07-12T13:09:44.32949Z","shell.execute_reply":"2021-07-12T13:09:44.338653Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 🧎事前処理分解終了。事前にカラム分読み込んで処理していた。\n### 時間があれば、コメント行を外して実行すると勉強になると思います。","metadata":{"_uuid":"cac7a3c0-abe6-4e42-a8dd-d33d3f6f843f","_cell_guid":"03e259ea-882b-4ba0-85c8-7453b26faec4","trusted":true}},{"cell_type":"markdown","source":"### 🎺Training\n### 学習部分を分解開始","metadata":{"_uuid":"d2c77d85-34c2-4d30-b263-f167041769f1","_cell_guid":"372bbf0f-c708-4369-97f2-ee91e73da160","trusted":true}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport gc\nfrom pathlib import Path\nfrom sklearn.metrics import mean_absolute_error\nfrom datetime import timedelta\nfrom functools import reduce\nfrom tqdm import tqdm\nimport lightgbm as lgbm\nimport mlb","metadata":{"_uuid":"bd56fc98-0afd-465b-97fd-c6487f1e9d2a","_cell_guid":"be9118a4-9c6c-4c65-b301-efcbcf7f5ff2","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":2.218923,"end_time":"2021-06-28T10:01:26.979346","exception":false,"start_time":"2021-06-28T10:01:24.760423","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-07-18T12:05:53.521167Z","iopub.execute_input":"2021-07-18T12:05:53.521747Z","iopub.status.idle":"2021-07-18T12:05:53.528108Z","shell.execute_reply.started":"2021-07-18T12:05:53.521707Z","shell.execute_reply":"2021-07-18T12:05:53.526949Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"BASE_DIR = Path('../input/mlb-player-digital-engagement-forecasting')\nTRAIN_DIR = Path('../input/mlb-pdef-train-dataset')","metadata":{"_uuid":"86bfe3df-c678-4768-81df-ff2e43df6c55","_cell_guid":"a52f476a-536f-4954-a7a7-e9b54a982d0b","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":0.025599,"end_time":"2021-06-28T10:01:27.024369","exception":false,"start_time":"2021-06-28T10:01:26.99877","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-07-18T11:40:18.371738Z","iopub.execute_input":"2021-07-18T11:40:18.372024Z","iopub.status.idle":"2021-07-18T11:40:18.376401Z","shell.execute_reply.started":"2021-07-18T11:40:18.371996Z","shell.execute_reply":"2021-07-18T11:40:18.375083Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"players = pd.read_csv(BASE_DIR / 'players.csv')\nplayers","metadata":{"_uuid":"c310e999-d7fc-4ad7-9e2f-a3b457482fd7","_cell_guid":"59531093-107f-4a6a-9b17-fb5afe854b64","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:46.936657Z","iopub.execute_input":"2021-07-12T13:09:46.937031Z","iopub.status.idle":"2021-07-12T13:09:47.000305Z","shell.execute_reply.started":"2021-07-12T13:09:46.936998Z","shell.execute_reply":"2021-07-12T13:09:46.999015Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"f4b3037d-13e8-4bf9-88bc-51c3564963ce","_cell_guid":"d3e79c39-ad20-4aed-90ce-4fa40ef94d07","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-18T12:07:19.059185Z","iopub.execute_input":"2021-07-18T12:07:19.059558Z","iopub.status.idle":"2021-07-18T12:07:19.754633Z","shell.execute_reply.started":"2021-07-18T12:07:19.059521Z","shell.execute_reply":"2021-07-18T12:07:19.75381Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!rm rosters_train.pkl","metadata":{"execution":{"iopub.status.busy":"2021-07-18T12:07:13.691386Z","iopub.execute_input":"2021-07-18T12:07:13.691808Z","iopub.status.idle":"2021-07-18T12:07:14.445722Z","shell.execute_reply.started":"2021-07-18T12:07:13.691771Z","shell.execute_reply":"2021-07-18T12:07:14.444521Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"targets = pd.read_pickle(TRAIN_DIR / 'nextDayPlayerEngagement_train.pkl')\ntargets.to_pickle('nextDayPlayerEngagement_train.pkl')\ntargets","metadata":{"_uuid":"4c667759-8f05-4b2c-94da-3250232a360c","_cell_guid":"9f14a772-3f07-40bc-9b06-0abd05952581","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-18T12:07:48.669873Z","iopub.execute_input":"2021-07-18T12:07:48.670255Z","iopub.status.idle":"2021-07-18T12:07:50.789597Z","shell.execute_reply.started":"2021-07-18T12:07:48.670214Z","shell.execute_reply":"2021-07-18T12:07:50.7888Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"scoresx = pd.read_pickle(TRAIN_DIR / 'playerBoxScores_train.pkl')\nscoresx","metadata":{"_uuid":"e3a3c4bd-c69d-43eb-8f96-46bce29ff909","_cell_guid":"5faeb464-3953-4648-b271-22b31a31f0b5","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-18T11:57:30.871701Z","iopub.execute_input":"2021-07-18T11:57:30.872062Z","iopub.status.idle":"2021-07-18T11:57:31.350104Z","shell.execute_reply.started":"2021-07-18T11:57:30.872035Z","shell.execute_reply":"2021-07-18T11:57:31.349122Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"rosters = pd.read_pickle(TRAIN_DIR / 'rosters_train.pkl')\nrosters.to_pickle('rosters_train.pkl')","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 他の人のデータを利用しているので、データを取り込んでpickle化しています。\n### もし、データの持ち主がデータセットを削除すると、もう動かなくなってしまいますから。(そんな悪い人はいないと信じたいですが）\n\n### データを公開しておいてスコアがあがり、いざ締め切りの時にデータセットを消されると、\n### すべてが台無しになります。エラーになると思います。それに備えてコピーして自分のデータセットにしておくんです。\n\n上の行を実行すると、右のoutput /kaggle/workingの下にファイルができます。<br>\nできたファイルの右あたりをクリックするとdownloadの文字がでてきます。<br>\nそれを押せばダウンロードできます。<br>\nデータセットを作るのは簡単で、ADD dataをクリックしてアップロードすれば完成です。","metadata":{}},{"cell_type":"code","source":"scores = scoresx.groupby(['playerId', 'date']).sum().reset_index()\n\ndel scoresx\nscores","metadata":{"_uuid":"35497f04-6943-4686-b539-b4e391aa6f7b","_cell_guid":"c403fdeb-d11c-4322-805b-18332e5ee303","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":4.541488,"end_time":"2021-06-28T10:01:31.584745","exception":false,"start_time":"2021-06-28T10:01:27.043257","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-07-12T13:09:51.347438Z","iopub.execute_input":"2021-07-12T13:09:51.347738Z","iopub.status.idle":"2021-07-12T13:09:52.342644Z","shell.execute_reply.started":"2021-07-12T13:09:51.347709Z","shell.execute_reply":"2021-07-12T13:09:52.341325Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### <font color=\"orange\">Python でデータ処理するライブラリの定番 Pandas の groupby<br>\ngroupby は、同じ値を持つデータをまとめて、それぞれの塊に対して共通の操作を行いたい時に使う。<br>\n■Aggregation<br>\nGroupBy.mean() のように、グループごとに値を求めて表を作るような操作を Aggregation と呼ぶ。このように GroupBy オブジェクトには Aggregation に使う関数が幾つか定義されているが、これらは agg() を使っても実装出来る。<br>\n-df.groupby('city').agg(np.mean)<br>\n■apply \n    \n    \n■Transformation","metadata":{"_uuid":"9dd8d018-9421-41e3-8c61-bb875848780a","_cell_guid":"446f5fb5-5d61-4cef-839d-8a7e3605ad8c","trusted":true}},{"cell_type":"markdown","source":"### 結構重要なパート<br>\n\n説明関数が沢山ありますが、余計なものも入っています。余計なものが入っているとスコアが悪くなります。<br>\nコメントアウトしているのは、余計な関数だから。<br>\n余計な関数かどうかは、LGBMのimportantではうまくいったことがありません。<br>\n地道に一個づつ、名前からして余計だなと思うのをコメントアウト、実行しスコアをみています。すごい大変ですが重要な部分です。\n","metadata":{}},{"cell_type":"code","source":"targets_cols = ['playerId', 'target1', 'target2', 'target3', 'target4', 'date']\nplayers_cols = ['playerId', 'primaryPositionName']\nrosters_cols = ['playerId', 'teamId', 'status', 'date']\nscores_cols = ['playerId', 'battingOrder', 'gamesPlayedBatting', 'flyOuts',\n       'groundOuts', 'runsScored', 'doubles', 'triples', 'homeRuns',\n       'strikeOuts', 'baseOnBalls', 'intentionalWalks', 'hits', 'hitByPitch',\n       'caughtStealing', 'stolenBases', 'atBats', 'groundIntoDoublePlay',\n       'groundIntoTriplePlay', 'plateAppearances', 'totalBases', 'rbi',\n       'leftOnBase', 'sacBunts',# 'sacFlies', 'catchersInterference',\n       'pickoffs', 'gamesPlayedPitching', 'gamesStartedPitching',\n       'completeGamesPitching', 'shutoutsPitching', 'winsPitching',\n       'airOutsPitching',#lossesPitching', #'flyOutsPitching', ', ###\n       'groundOutsPitching', 'runsPitching', 'doublesPitching',\n       'triplesPitching', 'homeRunsPitching', 'strikeOutsPitching',\n       'baseOnBallsPitching', 'intentionalWalksPitching', 'hitsPitching',\n       'hitByPitchPitching', 'atBatsPitching', 'caughtStealingPitching',\n       'stolenBasesPitching', 'inningsPitched', 'saveOpportunities',\n       'earnedRuns', 'battersFaced', 'outsPitching', 'pitchesThrown', 'balls',\n       'strikes', 'hitBatsmen', 'balks', 'wildPitches', 'pickoffsPitching',\n       'rbiPitching', 'gamesFinishedPitching', 'inheritedRunners',\n       'inheritedRunnersScored', 'catchersInterferencePitching',\n       'sacBuntsPitching', 'sacFliesPitching', 'saves',#'holds', 'blownSaves',\n       'assists', 'putOuts', 'errors', 'chances', 'date']\n\nfeature_cols = ['label_playerId', #'label_primaryPositionName',# 'label_teamId', \n       'battingOrder', 'gamesPlayedBatting', 'flyOuts','label_status', #　'〇label_status',\n       'groundOuts', 'runsScored', 'doubles', 'triples', 'homeRuns',\n       'strikeOuts', 'baseOnBalls', 'intentionalWalks', 'hits', 'hitByPitch',\n       'caughtStealing', 'stolenBases','atBats',  'groundIntoDoublePlay', #○'atBats', ' 'groundIntoDoublePlay',\n       'groundIntoTriplePlay', 'plateAppearances', 'totalBases', 'rbi',\n       'leftOnBase', #'sacBunts', #'sacFlies', 'catchersInterference', #'leftOnBase', '\n       'pickoffs', 'gamesPlayedPitching', 'gamesStartedPitching',\n       'completeGamesPitching', 'shutoutsPitching', 'winsPitching',\n       #'lossesPitching', 'flyOutsPitching', 'airOutsPitching',###\n       'groundOutsPitching', 'runsPitching', 'doublesPitching',\n       'triplesPitching', 'homeRunsPitching', 'strikeOutsPitching',\n       'baseOnBallsPitching',  'hitsPitching','intentionalWalksPitching',\n       'hitByPitchPitching', 'atBatsPitching', 'caughtStealingPitching',\n       'stolenBasesPitching', 'inningsPitched', 'saveOpportunities',\n       'earnedRuns', 'battersFaced', 'outsPitching', 'pitchesThrown', 'balls',\n       'strikes', 'hitBatsmen',  'wildPitches', 'pickoffsPitching','balks',#○'pickoffsPitching', \n       'rbiPitching', 'gamesFinishedPitching', 'inheritedRunners',\n       'inheritedRunnersScored', 'catchersInterferencePitching',\n       'sacBuntsPitching','saves','sacFliesPitching', #'holds', 'blownSaves',○'sacBuntsPitching', ○'sacFliesPitching' ○ 'sacFliesPitching'\n       'assists', 'putOuts', 'errors', 'chances','target1_mean',\n 'target1_median',\n 'target1_std',\n 'target1_min',\n 'target1_max',\n 'target1_prob',\n 'target2_mean',\n 'target2_median',\n 'target2_std',\n 'target2_min',\n 'target2_max',\n 'target2_prob',\n 'target3_mean',\n 'target3_median',\n 'target3_std',\n 'target3_min',\n 'target3_max',\n 'target3_prob',\n 'target4_mean',\n 'target4_median',\n 'target4_std',\n 'target4_min',\n 'target4_max',\n 'target4_prob']\n\nfeature_cols2 = ['label_playerId', 'label_primaryPositionName', 'label_teamId', #needed\n                 'label_status', 'battingOrder', 'gamesPlayedBatting', 'flyOuts',\n       'groundOuts', 'runsScored', 'doubles', 'triples', 'homeRuns',\n       'strikeOuts', 'baseOnBalls', 'hits', 'hitByPitch', 'intentionalWalks', #○'intentionalWalks'\n       'caughtStealing', 'stolenBases',  'groundIntoDoublePlay','atBats',#'〇atBats\n       'groundIntoTriplePlay', 'plateAppearances', 'totalBases', 'rbi',#○ rbi \n       'leftOnBase','sacBunts',# 'sacFlies', 'catchersInterference',#', 〇leftOnBase'  \n       'gamesPlayedPitching', 'gamesStartedPitching','pickoffs',   #〇'pickoffs' \n       'completeGamesPitching', 'shutoutsPitching', 'winsPitching',\n       #'airOutsPitching',# lossesPitching', #'flyOutsPitching', '',##〇\n       'groundOutsPitching', 'runsPitching', 'doublesPitching',\n       'triplesPitching', 'homeRunsPitching', 'strikeOutsPitching',\n       'baseOnBallsPitching', 'hitsPitching', 'intentionalWalksPitching',\n       'hitByPitchPitching', 'atBatsPitching', 'caughtStealingPitching',\n       'stolenBasesPitching', 'inningsPitched', 'saveOpportunities',\n       'earnedRuns', 'battersFaced', 'outsPitching', 'pitchesThrown', 'balls',\n       'strikes', 'hitBatsmen', 'wildPitches', 'pickoffsPitching',#'balks', \n       'rbiPitching', 'gamesFinishedPitching', 'inheritedRunners', \n       'inheritedRunnersScored', #'catchersInterferencePitching', \n       'sacFliesPitching', 'saves','sacBuntsPitching', #'holds', 'blownSaves'   #〇sacBuntsPitching'\n       'assists', 'putOuts', 'errors', 'chances','target1_mean',\n 'target1_median',\n 'target1_std',\n 'target1_min',\n 'target1_max',\n 'target1_prob',\n 'target2_mean',\n 'target2_median',\n 'target2_std',\n 'target2_min',\n 'target2_max',\n 'target2_prob',\n 'target3_mean',\n 'target3_median',\n 'target3_std',\n 'target3_min',\n 'target3_max',\n 'target3_prob',\n 'target4_mean',\n 'target4_median',\n 'target4_std',\n 'target4_min',\n 'target4_max',\n 'target4_prob',\n    'target1']","metadata":{"_uuid":"fb967569-0077-4690-8c8b-e71d87212155","_cell_guid":"a49e2899-3936-48e9-97be-b765ad363c26","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":0.04137,"end_time":"2021-06-28T10:01:31.645006","exception":false,"start_time":"2021-06-28T10:01:31.603636","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-07-12T13:09:52.346132Z","iopub.execute_input":"2021-07-12T13:09:52.346559Z","iopub.status.idle":"2021-07-12T13:09:52.366173Z","shell.execute_reply.started":"2021-07-12T13:09:52.346518Z","shell.execute_reply":"2021-07-12T13:09:52.364868Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"この説明変数の追加、削除はやりつくして飽きました。他のところをみてみよう。","metadata":{}},{"cell_type":"markdown","source":" これはどこかで事前につくっている。２０６１人分全部。　ken.Millerさん。すごいなあ。\n Millerさんのどこからコピーしたかわすれてしまいましたが、webscrapingというやり方があるみたいです。　https://www.kaggle.com/tensorchoko/web-scraping/","metadata":{"_uuid":"087e0f97-a937-45fa-b588-57aa988cf52a","_cell_guid":"0edb44f9-d1fc-423c-b9e7-00af81bb3c22","trusted":true}},{"cell_type":"code","source":"player_target_stats = pd.read_csv(\"../input/player-target-stats/player_target_stats.csv\")\nplayer_target_stats","metadata":{"_uuid":"fb55cb10-7c3d-4ca6-90c3-b84a0281a374","_cell_guid":"732e1a34-2657-42cf-81b0-4232bbcfec66","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:52.369267Z","iopub.execute_input":"2021-07-12T13:09:52.370348Z","iopub.status.idle":"2021-07-12T13:09:52.447325Z","shell.execute_reply.started":"2021-07-12T13:09:52.370282Z","shell.execute_reply":"2021-07-12T13:09:52.445932Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_names=player_target_stats.columns.values.tolist()\ndata_names","metadata":{"_uuid":"364a33a6-fc2d-4f7e-a082-a4d504b7a27f","_cell_guid":"f33db539-b8db-4c5e-b654-02e08fa3edc2","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":0.057639,"end_time":"2021-06-28T10:01:31.721487","exception":false,"start_time":"2021-06-28T10:01:31.663848","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-07-12T13:09:52.448743Z","iopub.execute_input":"2021-07-12T13:09:52.449065Z","iopub.status.idle":"2021-07-12T13:09:52.456404Z","shell.execute_reply.started":"2021-07-12T13:09:52.449032Z","shell.execute_reply":"2021-07-12T13:09:52.455073Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### <font color=\"orange\">pd.merge(), pd.DataFrame.merge()の使い方</font><br>\npd.merge()関数では第一引数leftと第二引数rightに結合する2つのpandas.DataFrameを指定する。<br>\n■結合方法 how<br>\n-内部結合（inner_join）: how='inner'<br>\n-左結合（left_join）: how='left'<br>\n-右結合（right_join）: how='right'<br>\n-外部結合（outer_join）: how='outer'<br>\n\n■引数<br>\n-明示的に指定する場合は引数onを使う。省略して問題ない場合も明示しておいたほうが分かりやすい。<br>\n-引数left_on, right_onでそれぞれのpandas.DataFrameの列名を別々に指定することも可能","metadata":{"_uuid":"a43f55f3-3092-4262-896b-d707e8afc9fe","_cell_guid":"1813a7f8-0bb7-4949-b636-32e051c361e6","trusted":true}},{"cell_type":"code","source":"train = targets[targets_cols].merge(players[players_cols], on=['playerId'], how='left')\ntrain","metadata":{"_uuid":"94df01a4-26b2-4910-8a0c-b6d25e1adaac","_cell_guid":"619c15af-d274-4cc0-995b-6484fc49de1d","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:52.458175Z","iopub.execute_input":"2021-07-12T13:09:52.458665Z","iopub.status.idle":"2021-07-12T13:09:53.090046Z","shell.execute_reply.started":"2021-07-12T13:09:52.45861Z","shell.execute_reply":"2021-07-12T13:09:53.088274Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = train.merge(rosters[rosters_cols], on=['playerId', 'date'], how='left')\ntrain","metadata":{"_uuid":"3791d32a-96cf-466b-bc3e-0f226fe67b8f","_cell_guid":"b6301f13-3bff-40e6-8003-61891b332e20","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:53.091956Z","iopub.execute_input":"2021-07-12T13:09:53.092348Z","iopub.status.idle":"2021-07-12T13:09:54.42514Z","shell.execute_reply.started":"2021-07-12T13:09:53.092307Z","shell.execute_reply":"2021-07-12T13:09:54.424123Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = train.merge(scores[scores_cols], on=['playerId', 'date'], how='left')\ntrain","metadata":{"_uuid":"8ba89cbc-2402-49f9-8d5b-168fba9abc06","_cell_guid":"6673100d-25e6-48ad-bf97-2dd7d2f25abc","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:54.426763Z","iopub.execute_input":"2021-07-12T13:09:54.427233Z","iopub.status.idle":"2021-07-12T13:09:58.00468Z","shell.execute_reply.started":"2021-07-12T13:09:54.427179Z","shell.execute_reply":"2021-07-12T13:09:58.003351Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = train.merge(player_target_stats, how='inner', left_on=[\"playerId\"],right_on=[\"playerId\"])\ntrain","metadata":{"_uuid":"06154cb8-839a-4c0e-9c21-bb941e499552","_cell_guid":"ec9a2d33-92ce-4baa-a6af-0a2803440873","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:09:58.006549Z","iopub.execute_input":"2021-07-12T13:09:58.00702Z","iopub.status.idle":"2021-07-12T13:10:04.669793Z","shell.execute_reply.started":"2021-07-12T13:09:58.006966Z","shell.execute_reply":"2021-07-12T13:10:04.668478Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"メモリ節約のため不要データは消す。","metadata":{"_uuid":"207e2e14-a992-4573-9c39-9e616d309537","_cell_guid":"db5fce0c-3d3e-412a-915f-9b333342e885","trusted":true}},{"cell_type":"code","source":"del rosters\ndel scores\nplayer_target_stats\ngc.collect()","metadata":{"_uuid":"e4c03d22-536a-4e27-8260-4fb458a60153","_cell_guid":"17b7cbe7-a30d-459e-89ad-b1f2de4bfe24","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:10:04.671763Z","iopub.execute_input":"2021-07-12T13:10:04.672243Z","iopub.status.idle":"2021-07-12T13:10:04.811393Z","shell.execute_reply.started":"2021-07-12T13:10:04.672174Z","shell.execute_reply":"2021-07-12T13:10:04.810103Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"ここでは文字列を一意の数字に置き換えている。","metadata":{"_uuid":"9805d1c0-e11b-42f9-b2c0-1e65524067d9","_cell_guid":"8590eb09-d941-4369-8769-f01517d9af88","trusted":true}},{"cell_type":"code","source":"player2num = {c: i for i, c in enumerate(train['playerId'].unique())}\nplayer2num[628317] #playerid to 一意な番号を対応させている。","metadata":{"_uuid":"58cc6930-9abd-439b-aceb-5f75e9fb1190","_cell_guid":"086a6486-5f7e-4c14-8bf8-a7a95523de36","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:10:04.81307Z","iopub.execute_input":"2021-07-12T13:10:04.813565Z","iopub.status.idle":"2021-07-12T13:10:04.84486Z","shell.execute_reply.started":"2021-07-12T13:10:04.813513Z","shell.execute_reply":"2021-07-12T13:10:04.843683Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"position2num = {c: i for i, c in enumerate(train['primaryPositionName'].unique())}\nposition2num","metadata":{"_uuid":"0bcdc8e6-9fec-4e8c-819a-528c18a4f7f5","_cell_guid":"e43315cb-5960-41a3-9f42-53ed7a8067a2","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:10:04.846514Z","iopub.execute_input":"2021-07-12T13:10:04.84695Z","iopub.status.idle":"2021-07-12T13:10:05.033631Z","shell.execute_reply.started":"2021-07-12T13:10:04.846902Z","shell.execute_reply":"2021-07-12T13:10:05.032548Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"teamid2num = {c: i for i, c in enumerate(train['teamId'].unique())}\nteamid2num","metadata":{"_uuid":"f47130d3-cd87-4a87-82ac-184a0ad55ab6","_cell_guid":"bc7645d4-0ba5-438b-b20a-35da9d142242","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:10:05.034977Z","iopub.execute_input":"2021-07-12T13:10:05.035355Z","iopub.status.idle":"2021-07-12T13:10:05.064404Z","shell.execute_reply.started":"2021-07-12T13:10:05.035322Z","shell.execute_reply":"2021-07-12T13:10:05.063312Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"status2num = {c: i for i, c in enumerate(train['status'].unique())}\nstatus2num","metadata":{"_uuid":"9476c388-1c04-49c5-aba5-64c6dcc9aaf3","_cell_guid":"d219e063-a6e8-40d1-99d5-23f2e186e921","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:10:05.066021Z","iopub.execute_input":"2021-07-12T13:10:05.066375Z","iopub.status.idle":"2021-07-12T13:10:05.345917Z","shell.execute_reply.started":"2021-07-12T13:10:05.066342Z","shell.execute_reply":"2021-07-12T13:10:05.344532Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train['label_playerId'] = train['playerId'].map(player2num)","metadata":{"_uuid":"67cc751f-54b5-4eed-85bd-69f25555af03","_cell_guid":"8951706c-fb7e-47ee-9564-d25cfbaa438f","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:10:05.347717Z","iopub.execute_input":"2021-07-12T13:10:05.348229Z","iopub.status.idle":"2021-07-12T13:10:05.392015Z","shell.execute_reply.started":"2021-07-12T13:10:05.348179Z","shell.execute_reply":"2021-07-12T13:10:05.390909Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train['label_primaryPositionName'] = train['primaryPositionName'].map(position2num) #position2は辞書形式で文字を数字に変換している\ntrain['label_primaryPositionName']","metadata":{"_uuid":"41149fbe-b1ca-4982-abf9-85b12f55a472","_cell_guid":"afafd627-e8dc-46ae-8aa3-fb923e977129","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:10:05.393751Z","iopub.execute_input":"2021-07-12T13:10:05.394174Z","iopub.status.idle":"2021-07-12T13:10:05.639459Z","shell.execute_reply.started":"2021-07-12T13:10:05.394125Z","shell.execute_reply":"2021-07-12T13:10:05.638409Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train['label_teamId'] = train['teamId'].map(teamid2num)\ntrain['label_teamId']","metadata":{"_uuid":"c31f4a21-ea62-42db-a95c-13df210bca0b","_cell_guid":"8cfab384-eb13-414e-92b4-0b4a70a1f17b","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:10:05.640944Z","iopub.execute_input":"2021-07-12T13:10:05.641276Z","iopub.status.idle":"2021-07-12T13:10:05.702191Z","shell.execute_reply.started":"2021-07-12T13:10:05.641237Z","shell.execute_reply":"2021-07-12T13:10:05.700798Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train['label_status'] = train['status'].map(status2num)\ntrain['label_status']","metadata":{"_uuid":"67ffeccb-127b-4882-aab6-8d9d5330d159","_cell_guid":"bfa4a0ab-87c1-4707-a756-a184987b240e","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":8.063224,"end_time":"2021-06-28T10:01:39.80402","exception":false,"start_time":"2021-06-28T10:01:31.740796","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-07-12T13:10:05.703814Z","iopub.execute_input":"2021-07-12T13:10:05.704291Z","iopub.status.idle":"2021-07-12T13:10:06.084358Z","shell.execute_reply.started":"2021-07-12T13:10:05.704228Z","shell.execute_reply":"2021-07-12T13:10:06.083144Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"trainデータとvalidデータにわけている。<br>","metadata":{"_uuid":"30f1433b-132b-4ee8-897e-868d4e8c46bf","_cell_guid":"f60688fc-57ac-4322-aedc-2afa2219e207","trusted":true}},{"cell_type":"code","source":"train_X = train[feature_cols]\ntrain_y = train[['target1', 'target2', 'target3', 'target4']]\n\n_index = (train['date'] < 20210401)\nx_train1 = train_X.loc[_index].reset_index(drop=True)\ny_train1 = train_y.loc[_index].reset_index(drop=True)\nx_valid1 = train_X.loc[~_index].reset_index(drop=True)\ny_valid1 = train_y.loc[~_index].reset_index(drop=True)","metadata":{"_uuid":"27dfbf2d-0555-4f34-ad8f-f1b03a176195","_cell_guid":"bd5b7cca-f590-48be-9c7b-1057d33833cd","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":4.218152,"end_time":"2021-06-28T10:01:44.04165","exception":false,"start_time":"2021-06-28T10:01:39.823498","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-07-12T13:10:06.086269Z","iopub.execute_input":"2021-07-12T13:10:06.086662Z","iopub.status.idle":"2021-07-12T13:10:13.131589Z","shell.execute_reply.started":"2021-07-12T13:10:06.086628Z","shell.execute_reply":"2021-07-12T13:10:13.130593Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def fit_lgbm(x_train, y_train, x_valid, y_valid, params: dict=None, verbose=100):\n    oof_pred = np.zeros(len(y_valid), dtype=np.float32)\n    model = lgbm.LGBMRegressor(**params) #回帰型lgb\n    model.fit(x_train, y_train, \n        eval_set=[(x_valid, y_valid)],  \n        early_stopping_rounds=verbose, \n        verbose=verbose)\n    oof_pred = model.predict(x_valid)\n    score = mean_absolute_error(oof_pred, y_valid)\n    print('mae:', score)\n    return oof_pred, model, score","metadata":{"_uuid":"023f44bf-220d-4a0b-81a5-8a27214f4ab3","_cell_guid":"e470e5ce-78c8-4b5c-9bda-83b4c32ccc8e","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:10:13.133117Z","iopub.execute_input":"2021-07-12T13:10:13.133797Z","iopub.status.idle":"2021-07-12T13:10:13.142696Z","shell.execute_reply.started":"2021-07-12T13:10:13.133743Z","shell.execute_reply":"2021-07-12T13:10:13.141605Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 2021/7/12 metricをl1=>l2に変更してみる。","metadata":{}},{"cell_type":"code","source":"prm1={'objective': 'mae',\n 'metric': 'l2',\n 'feature_pre_filter': False,\n 'lambda_l1': 1.146853310507615e-06,\n 'lambda_l2': 0.5249994991618241,\n 'num_leaves': 255,\n 'feature_fraction': 0.48000000000000004,\n 'bagging_fraction': 0.4073948386925109,\n 'bagging_freq': 6,\n 'min_child_samples': 5,\n 'num_iterations': 1000,\n 'early_stopping_round': 50,#None,\n 'n_estimators': 5000,#3633,\n 'learning_rate': 0.08} #0.08046301304430488}","metadata":{"_uuid":"ad3c85d6-63e5-4c25-8ca7-795cb84e01a1","_cell_guid":"077df867-2e57-4e70-a45c-a4316ca123db","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:10:13.149154Z","iopub.execute_input":"2021-07-12T13:10:13.149568Z","iopub.status.idle":"2021-07-12T13:10:13.156493Z","shell.execute_reply.started":"2021-07-12T13:10:13.149527Z","shell.execute_reply":"2021-07-12T13:10:13.155363Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"oof1, model1, score1 = fit_lgbm(\n    x_train1, y_train1['target1'],\n    x_valid1, y_valid1['target1'],\n    prm1\n )","metadata":{"_uuid":"709b85d9-fd90-425f-ba1e-e9a78965b988","_cell_guid":"a868d413-64fc-4d73-9193-b8feafdcf05b","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:10:13.159455Z","iopub.execute_input":"2021-07-12T13:10:13.160121Z","iopub.status.idle":"2021-07-12T13:12:32.358261Z","shell.execute_reply.started":"2021-07-12T13:10:13.160068Z","shell.execute_reply":"2021-07-12T13:12:32.357127Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#del train\ndel x_train1\n#del x_train2\ndel y_train1\n#del y_train2\ndel x_valid1\n#del x_valid2\ndel y_valid1\n#del y_valid2\ndel train_X\ndel train_y\ngc.collect()","metadata":{"_uuid":"2c042e39-8678-4fb5-87d8-935228c474a8","_cell_guid":"1d5c37a1-e0ee-4f7e-96b8-407cd10c339e","collapsed":false,"papermill":{"duration":5.207389,"end_time":"2021-06-28T10:01:49.270829","exception":false,"start_time":"2021-06-28T10:01:44.06344","status":"completed"},"tags":[],"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:12:32.362416Z","iopub.execute_input":"2021-07-12T13:12:32.364705Z","iopub.status.idle":"2021-07-12T13:12:32.529662Z","shell.execute_reply.started":"2021-07-12T13:12:32.364613Z","shell.execute_reply":"2021-07-12T13:12:32.528397Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:10165638-f1e6-48dd-98e2-845b865110ff.png)","metadata":{"_uuid":"14408791-6924-4c2e-94c9-d4c995c97b51","_cell_guid":"37fa57c5-2991-4ecb-9e74-26691e31ec96","trusted":true},"attachments":{"10165638-f1e6-48dd-98e2-845b865110ff.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"### optunaはlightGBMのハイパーパラメータでいいものをさがしてくれる味方です。\n\n### optuna一度にやると処理時間　軽く９時間オーバーです。今はコメントアウトしています。\n\n### やったことない人は、コメントアウトはずしてうごかしてみてください。","metadata":{"_uuid":"2843da77-0f39-423d-8db1-8d745816e761","_cell_guid":"27daf47d-edb1-4a6a-a56e-08eb004ce41d","trusted":true}},{"cell_type":"code","source":"import optuna \nimport optuna.integration.lightgbm as lgbo\nimport lightgbm as lgb\n\nparams = { 'objective': 'mae', 'metric': 'mae' } \n#lgb_train1 = lgb.Dataset(x_train1, y_train1['target1'])\n#lgb_valid1 = lgb.Dataset(x_valid1, y_valid1['target1'])\n#model1 = lgbo.train(params, lgb_train1, valid_sets=[lgb_valid1], verbose_eval=100)\n#model1.params = {'n_estimators': 3633, 'learning_rate': 0.08046301304430488}\n#model1.params","metadata":{"_uuid":"1af9e480-c0ba-4e8b-aa5b-478802570fb8","_cell_guid":"88b1a202-ddd3-41fc-8f82-0fb6fabecb40","collapsed":false,"papermill":{"duration":0.710821,"end_time":"2021-06-28T10:01:50.004281","exception":false,"start_time":"2021-06-28T10:01:49.29346","status":"completed"},"tags":[],"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:12:32.531153Z","iopub.execute_input":"2021-07-12T13:12:32.531491Z","iopub.status.idle":"2021-07-12T13:12:33.374655Z","shell.execute_reply.started":"2021-07-12T13:12:32.531457Z","shell.execute_reply":"2021-07-12T13:12:33.373566Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_X = train[feature_cols2]\ntrain_y = train[['target1', 'target2', 'target3', 'target4']]\n\n_index = (train['date'] < 20210401)\nx_train2 = train_X.loc[_index].reset_index(drop=True)\ny_train2 = train_y.loc[_index].reset_index(drop=True)\nx_valid2 = train_X.loc[~_index].reset_index(drop=True)\ny_valid2 = train_y.loc[~_index].reset_index(drop=True)","metadata":{"_uuid":"979adc19-22a5-47b8-bbdc-2544884d0762","_cell_guid":"458f497c-095d-4943-8665-d11927ce3f96","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:12:33.376339Z","iopub.execute_input":"2021-07-12T13:12:33.37681Z","iopub.status.idle":"2021-07-12T13:12:36.483986Z","shell.execute_reply.started":"2021-07-12T13:12:33.37676Z","shell.execute_reply":"2021-07-12T13:12:36.482832Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del train\ndel train_X\ndel train_y\ngc.collect()","metadata":{"_uuid":"c0422cc1-51d1-4393-96de-d33de4c2bfe3","_cell_guid":"a13b1a40-6386-4bf4-8728-782f718b07e4","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:12:36.485627Z","iopub.execute_input":"2021-07-12T13:12:36.485979Z","iopub.status.idle":"2021-07-12T13:12:36.649154Z","shell.execute_reply.started":"2021-07-12T13:12:36.485942Z","shell.execute_reply":"2021-07-12T13:12:36.647978Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n#lgb_train2 = lgb.Dataset(x_train2, y_train2['target2'])\n#lgb_valid2 = lgb.Dataset(x_valid2, y_valid2['target2'])\n#model2 = lgbo.train(params, lgb_train2, valid_sets=[lgb_valid2], verbose_eval=100)\n#model2.params ={'n_estimators': 80, 'learning_rate': 0.1}\n#model2.params","metadata":{"_uuid":"01ec89f4-145d-466c-96fd-d0f58e7a9c46","_cell_guid":"d453bd44-713f-4792-8c85-a829db277156","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:12:36.650871Z","iopub.execute_input":"2021-07-12T13:12:36.651222Z","iopub.status.idle":"2021-07-12T13:12:36.661888Z","shell.execute_reply.started":"2021-07-12T13:12:36.65117Z","shell.execute_reply":"2021-07-12T13:12:36.660611Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"ba35b44f-00c6-401f-8e40-2435ca2a6016","_cell_guid":"046e3526-0976-4ef0-afc4-21d39e5c231e","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n#lgb_train2 = lgb.Dataset(x_train2, y_train2['target3'])\n#lgb_valid2 = lgb.Dataset(x_valid2, y_valid2['target3'])\n#model3 = lgbo.train(params, lgb_train2, valid_sets=[lgb_valid2], verbose_eval=100)\n#model3.params ={'n_estimators': 9868, 'learning_rate': 0.10528150510326864}\n#model3.params","metadata":{"_uuid":"37cfc0ca-9306-4ec8-9e74-5355bf34bcc3","_cell_guid":"7a671b4c-a583-4959-a27a-73c2a8c3eb0d","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:12:36.663713Z","iopub.execute_input":"2021-07-12T13:12:36.664137Z","iopub.status.idle":"2021-07-12T13:12:36.678398Z","shell.execute_reply.started":"2021-07-12T13:12:36.66409Z","shell.execute_reply":"2021-07-12T13:12:36.676959Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"ab246368-1b63-4cf1-89bc-ba583d0233bb","_cell_guid":"6ed02d84-515f-4ffe-94e9-76288d6b0659","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n#lgb_train2 = lgb.Dataset(x_train2, y_train2['target4'])\n#lgb_valid2 = lgb.Dataset(x_valid2, y_valid2['target4'])\n#model4 = lgbo.train(params, lgb_train2, valid_sets=[lgb_valid2], verbose_eval=100)\n#model4.params = {'n_estimators': 9868, 'learning_rate': 0.10528150510326864}\n#model4.params","metadata":{"_uuid":"23f73c77-ddb4-4b09-abff-c864679da686","_cell_guid":"b1316a8a-1f1b-4e63-810f-15a6111d80c4","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:12:36.680291Z","iopub.execute_input":"2021-07-12T13:12:36.680826Z","iopub.status.idle":"2021-07-12T13:12:36.697593Z","shell.execute_reply.started":"2021-07-12T13:12:36.680769Z","shell.execute_reply":"2021-07-12T13:12:36.696539Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"f3d1c93c-4f79-4a04-8d7b-031541f754a5","_cell_guid":"fb9f4026-7304-4155-aee4-8c52d8c362fa","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### パラメータ設定(Optunaで出力されたもの。一部は手入力）\n### n_estimators,learning_rateはOptunaで出力されない。これをどうやって決めるかわからない。Gridserchとかでやるのかなあ。","metadata":{"_uuid":"762b6270-e813-4411-a983-05fa028b9887","_cell_guid":"fd452b50-6c49-4d6c-b31f-c471c3469d87","trusted":true}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"\nprm2={'objective': 'mae',\n 'metric': 'l2',\n 'feature_pre_filter': False,\n 'lambda_l1': 0.023375452815085587,\n 'lambda_l2': 0.02276139446807019,\n 'num_leaves': 8,\n 'feature_fraction': 0.92,\n 'bagging_fraction': 0.6495829305086558,\n 'bagging_freq': 3,\n 'min_child_samples': 20,\n 'num_iterations': 1000,\n 'early_stopping_round': 50,#None,\n 'n_estimators': 1000,#80, \n 'learning_rate': 0.1}#0.1}\n\nprm3={'objective': 'mae',\n 'metric': 'l2',\n 'feature_pre_filter': False,\n 'lambda_l1': 0.0,\n 'lambda_l2': 0.0,\n 'num_leaves': 49,\n 'feature_fraction': 0.6,\n 'bagging_fraction': 1.0,\n 'bagging_freq': 0,\n 'min_child_samples': 20,\n 'num_iterations': 1000,\n 'early_stopping_round': 50,#None,\n 'n_estimators': 10000,#9868,\n 'learning_rate': 0.05} #0.0528150510326864}\n\nprm4 ={'objective': 'mae',\n 'metric': 'l2',\n 'feature_pre_filter': False,\n 'lambda_l1': 0.0,\n 'lambda_l2': 0.0,\n 'num_leaves': 254,\n 'feature_fraction': 0.4,\n 'bagging_fraction': 1.0,\n 'bagging_freq': 0,\n 'min_child_samples': 50,\n 'num_iterations': 1000,\n 'early_stopping_round': 50,#None,\n 'n_estimators': 1000, #9868, \n 'learning_rate': 0.05}#0528150510326864,}","metadata":{"_uuid":"8617e176-409a-4521-a505-d99a4908a28a","_cell_guid":"3aec2f48-b5fb-49fa-8e52-81a28ae31c61","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:12:36.699485Z","iopub.execute_input":"2021-07-12T13:12:36.700049Z","iopub.status.idle":"2021-07-12T13:12:36.713671Z","shell.execute_reply.started":"2021-07-12T13:12:36.700005Z","shell.execute_reply":"2021-07-12T13:12:36.712133Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\n# training lightgbm\n\nparams1 = {'objective':'mae','reg_alpha': 0.14947461820098767, 'reg_lambda': 0.10185644384043743, 'n_estimators': 3633, 'learning_rate': 0.08046301304430488, 'num_leaves': 674, 'feature_fraction': 0.9101240539122566, 'bagging_fraction': 0.9884451442950513, 'bagging_freq': 8, 'min_child_samples': 51}\n\nparams2 = {\n 'objective':'mae',\n 'reg_alpha': 0.1,\n 'reg_lambda': 0.1, \n 'n_estimators': 80,\n 'learning_rate': 0.1,\n 'random_state': 42,\n \"num_leaves\": 22\n}\n\nparams4 = {'objective':'mae','reg_alpha': 0.016468100279441976, 'reg_lambda': 0.09128335764019105, 'n_estimators': 9868, 'learning_rate': 0.10528150510326864, 'num_leaves': 157, 'feature_fraction': 0.5419185713426886, 'bagging_fraction': 0.2637405128936662, 'bagging_freq': 19, 'min_child_samples': 71}\n\n\nparams = {\n 'objective':'mae',\n 'reg_alpha': 0.1,\n 'reg_lambda': 0.1, \n 'n_estimators': 10000,\n 'learning_rate': 0.1,\n 'random_state': 42,\n \"num_leaves\": 100\n}\n\n\noof2, model2, score2 = fit_lgbm(\n    x_train2, y_train2['target2'],\n    x_valid2, y_valid2['target2'],\n    prm2\n)\n\noof3, model3, score3 = fit_lgbm(\n    x_train2, y_train2['target3'],\n    x_valid2, y_valid2['target3'],\n    prm3\n)\n\noof4, model4, score4 = fit_lgbm(\n    x_train2, y_train2['target4'],\n    x_valid2, y_valid2['target4'],\n    prm4\n)\nscore = (score1+score2+score3+score4) / 4\nprint(f'score: {score}')","metadata":{"_uuid":"fc275502-9b28-40a9-a7aa-6239fd75febf","_cell_guid":"f7e0da19-9e46-4842-bea2-b3f078d86fe7","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":481.729929,"end_time":"2021-06-28T10:09:51.754524","exception":false,"start_time":"2021-06-28T10:01:50.024595","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-07-12T13:12:36.715441Z","iopub.execute_input":"2021-07-12T13:12:36.715877Z","iopub.status.idle":"2021-07-12T13:18:37.375584Z","shell.execute_reply.started":"2021-07-12T13:12:36.715833Z","shell.execute_reply":"2021-07-12T13:18:37.374589Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n#del x_train1\ndel x_train2\n#del y_train1\ndel y_train2\n#del x_valid1\ndel x_valid2\n#del y_valid1\ndel y_valid2","metadata":{"_uuid":"660b4316-0143-438c-ab15-2a3c25ac90fc","_cell_guid":"5e42e3ba-8923-4751-99ae-6e17d2377dcc","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:18:37.379668Z","iopub.execute_input":"2021-07-12T13:18:37.381679Z","iopub.status.idle":"2021-07-12T13:18:37.395178Z","shell.execute_reply.started":"2021-07-12T13:18:37.381625Z","shell.execute_reply":"2021-07-12T13:18:37.394023Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 📲予測","metadata":{"_uuid":"6a12d090-ae80-47b2-a883-fb1094ec1ce3","_cell_guid":"54d2c147-92d2-4e62-a77a-d963eb77504b","trusted":true}},{"cell_type":"code","source":"players_cols = ['playerId', 'primaryPositionName']\nrosters_cols = ['playerId', 'teamId', 'status']\nscores_cols = ['playerId', 'battingOrder', 'gamesPlayedBatting', 'flyOuts',\n       'groundOuts', 'runsScored', 'doubles', 'triples', 'homeRuns',\n       'strikeOuts', 'baseOnBalls', 'intentionalWalks', 'hits', 'hitByPitch',\n       'caughtStealing', 'stolenBases', 'atBats','groundIntoDoublePlay',\n       'groundIntoTriplePlay', 'plateAppearances', 'totalBases', 'rbi',\n       'leftOnBase', 'sacBunts',# 'sacFlies', 'catchersInterference',\n       'pickoffs', 'gamesPlayedPitching', 'gamesStartedPitching',\n       'completeGamesPitching', 'shutoutsPitching', 'winsPitching',\n       'airOutsPitching',#'lossesPitching', #'flyOutsPitching', '###\n       'groundOutsPitching', 'runsPitching', 'doublesPitching',\n       'triplesPitching', 'homeRunsPitching', 'strikeOutsPitching',\n       'baseOnBallsPitching', 'intentionalWalksPitching', 'hitsPitching',\n       'hitByPitchPitching', 'atBatsPitching', 'caughtStealingPitching',\n       'stolenBasesPitching', 'inningsPitched', 'saveOpportunities',\n       'earnedRuns', 'battersFaced', 'outsPitching', 'pitchesThrown', 'balls',\n       'strikes', 'hitBatsmen', 'balks', 'wildPitches', 'pickoffsPitching',\n       'rbiPitching', 'gamesFinishedPitching', 'inheritedRunners',\n       'inheritedRunnersScored', 'catchersInterferencePitching',\n       'sacBuntsPitching', 'sacFliesPitching', 'saves', #'holds', 'blownSaves',\n       'assists', 'putOuts', 'errors', 'chances']\n\nnull = np.nan\ntrue = True\nfalse = False","metadata":{"_uuid":"eb7a3078-9746-48b3-abc4-aaf58bd8cba8","_cell_guid":"ee5802ab-edd0-4492-8f1a-9508c92808e1","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":0.043485,"end_time":"2021-06-28T10:09:51.881882","exception":false,"start_time":"2021-06-28T10:09:51.838397","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-07-12T13:18:37.396713Z","iopub.execute_input":"2021-07-12T13:18:37.397235Z","iopub.status.idle":"2021-07-12T13:18:37.411668Z","shell.execute_reply.started":"2021-07-12T13:18:37.39716Z","shell.execute_reply":"2021-07-12T13:18:37.410337Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nfrom datetime import timedelta\nfrom tqdm import tqdm\nimport gc\nfrom functools import reduce\nfrom sklearn.model_selection import StratifiedKFold\n\nROOT_DIR = \"../input/mlb-player-digital-engagement-forecasting\"\n\n#=======================#\ndef flatten(df, col):\n    du = (df.pivot(index=\"playerId\", columns=\"EvalDate\", \n               values=col).add_prefix(f\"{col}_\").\n      rename_axis(None, axis=1).reset_index())\n    return du\n\n#============================#\ndef reducer(left, right):\n    return left.merge(right, on=\"playerId\")\n\n#========================\n\nTGTCOLS = [\"target1\",\"target2\",\"target3\",\"target4\"]\ndef train_lag(df, lag=1):\n    dp = df[[\"playerId\",\"EvalDate\"]+TGTCOLS].copy()\n    dp[\"EvalDate\"]  =dp[\"EvalDate\"] + timedelta(days=lag) \n    df = df.merge(dp, on=[\"playerId\", \"EvalDate\"], suffixes=[\"\",f\"_{lag}\"], how=\"left\")\n    return df\n\n#=================================\ndef test_lag(sub):\n    sub[\"playerId\"] = sub[\"date_playerId\"].apply(lambda s: int(  s.split(\"_\")[1]  ) )\n    assert sub.date.nunique() == 1\n    dte = sub[\"date\"].unique()[0]\n    \n    eval_dt = pd.to_datetime(dte, format=\"%Y%m%d\")\n    dtes = [eval_dt + timedelta(days=-k) for k in LAGS]\n    mp_dtes = {eval_dt + timedelta(days=-k):k for k in LAGS}\n    \n    sl = LAST.loc[LAST.EvalDate.between(dtes[-1], dtes[0]), [\"EvalDate\",\"playerId\"]+TGTCOLS].copy()\n    sl[\"EvalDate\"] = sl[\"EvalDate\"].map(mp_dtes)\n    du = [flatten(sl, col) for col in TGTCOLS]\n    du = reduce(reducer, du)\n    return du, eval_dt\n    #\n#===============","metadata":{"_uuid":"468e473c-fbeb-4ee3-ba7a-cac1191e4574","_cell_guid":"85788140-5520-4b31-9a90-fef08d56f47c","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:18:37.413173Z","iopub.execute_input":"2021-07-12T13:18:37.413497Z","iopub.status.idle":"2021-07-12T13:18:37.431916Z","shell.execute_reply.started":"2021-07-12T13:18:37.413466Z","shell.execute_reply":"2021-07-12T13:18:37.430835Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### <font color=\"orange\"> Pandas.pivot</font><br>\n列方向に並んだデータを行方向に並べ替えたり、行方向に並んだデータを列方向に並べ替えたりして、データの構造を再形成できる。<br>\n■列から行へピボット: stack()<br>\n-stack()メソッドを呼ぶと列方向に並んでいたデータが行方向に並べ替えられる。縦に積み重なる（= stack）イメージ。<br>\n■行から列へピボット: unstack()<br>\n-stack()で取得したpandas.Seriesからunstack()を呼ぶと元に戻る。行方向に並んでいたデータが列方向に並べ替えられる。<br>\n\n＜例＞\nクロス集計したい時や、ちょっとややこしいグラフをつくる際などに必要になります。<br>\nname blood_type   class state<br>\n0   Alice          A    high    NY<br>\n1     Bob          B  middle    OH<br>\n2   Chris         AB     low    ND<br>\n3   David          B     low    OH<br>\n4    Evan          O     low    NY<br>\n5  Fabian          O  middle    OK<br>\n6    Gari          A    high    DC<br>\n\n<n>In [4]: df.pivot(index=\"class\",columns=\"state\",values=\"name\")</n><br>\nOut[4]:<br>\nstate     DC     ND     NY     OH      OK<br>\nclass                                    <br>\nhigh    Gari    NaN  Alice    NaN     NaN<br>\nlow      NaN  Chris   Evan  David     NaN<br>\nmiddle   NaN    NaN    NaN    Bob  Fabian<br>","metadata":{"_uuid":"0666db88-72ba-47fa-b6ad-a3405d51df33","_cell_guid":"4f3da852-be71-4902-a12c-7c9568b300fc","trusted":true}},{"cell_type":"code","source":"tr = pd.read_csv(\"../input/mlb-data/target.csv\") #外部データ\nprint(tr.shape)\ngc.collect()\n\ntr[\"EvalDate\"] = pd.to_datetime(tr[\"EvalDate\"])\ntr[\"EvalDate\"] = tr[\"EvalDate\"] + timedelta(days=-1)\ntr[\"EvalYear\"] = tr[\"EvalDate\"].dt.year\ntr","metadata":{"_uuid":"e54fe24a-4864-428e-baf5-b0e3ea7ec526","_cell_guid":"0284aa3c-d213-466f-a91f-866e1a145db3","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:18:37.43347Z","iopub.execute_input":"2021-07-12T13:18:37.433775Z","iopub.status.idle":"2021-07-12T13:18:42.711671Z","shell.execute_reply.started":"2021-07-12T13:18:37.433746Z","shell.execute_reply":"2021-07-12T13:18:42.709758Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"MED_DF = tr.groupby([\"playerId\",\"EvalYear\"])[TGTCOLS].median().reset_index()\nMED_DF","metadata":{"_uuid":"7741c4f1-73fa-45fc-a0d6-ac67d4f8ce5e","_cell_guid":"79132a60-681b-4126-972f-0fc9f08f8d41","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:18:42.713894Z","iopub.execute_input":"2021-07-12T13:18:42.714588Z","iopub.status.idle":"2021-07-12T13:18:43.275697Z","shell.execute_reply.started":"2021-07-12T13:18:42.714531Z","shell.execute_reply":"2021-07-12T13:18:43.274392Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"MEDCOLS = [\"tgt1_med\",\"tgt2_med\", \"tgt3_med\", \"tgt4_med\"]\nMED_DF.columns = [\"playerId\",\"EvalYear\"] + MEDCOLS\nMED_DF.columns","metadata":{"_uuid":"13ee3124-0aac-46e5-9fc8-f996584d70c2","_cell_guid":"d87394a5-1d9a-4680-8c7b-accbec7a8807","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:18:43.277235Z","iopub.execute_input":"2021-07-12T13:18:43.277692Z","iopub.status.idle":"2021-07-12T13:18:43.286049Z","shell.execute_reply.started":"2021-07-12T13:18:43.277583Z","shell.execute_reply":"2021-07-12T13:18:43.284872Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"LAGS = list(range(1,21))\nFECOLS = [f\"{col}_{lag}\" for lag in reversed(LAGS) for col in TGTCOLS]\nFECOLS","metadata":{"_uuid":"6e92140f-913b-4348-af6d-34fe8ec9e2ee","_cell_guid":"cec71fb2-a770-4029-80fb-6aa16974dc4d","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:18:43.287953Z","iopub.execute_input":"2021-07-12T13:18:43.28846Z","iopub.status.idle":"2021-07-12T13:18:43.302156Z","shell.execute_reply.started":"2021-07-12T13:18:43.288408Z","shell.execute_reply":"2021-07-12T13:18:43.300695Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for lag in tqdm(LAGS):\n    tr = train_lag(tr, lag=lag)\n    gc.collect()\n    \ntr","metadata":{"_uuid":"f78aa512-4abe-47db-8b75-59f5525315a5","_cell_guid":"7e9c3836-c61f-4cae-9b9e-ebdffa7cd967","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:18:43.303641Z","iopub.execute_input":"2021-07-12T13:18:43.304103Z","iopub.status.idle":"2021-07-12T13:19:54.032834Z","shell.execute_reply.started":"2021-07-12T13:18:43.304053Z","shell.execute_reply":"2021-07-12T13:19:54.03173Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tr = tr.sort_values(by=[\"playerId\", \"EvalDate\"])\nprint(tr.shape)\ntr = tr.dropna()\nprint(tr.shape)\ntr = tr.merge(MED_DF, on=[\"playerId\",\"EvalYear\"])\ngc.collect()\ntr","metadata":{"_uuid":"33f06126-3969-497b-85d4-14afc8734251","_cell_guid":"23d6ce59-ad53-44b3-ab92-4d009fc51e50","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:19:54.034457Z","iopub.execute_input":"2021-07-12T13:19:54.034896Z","iopub.status.idle":"2021-07-12T13:20:06.080433Z","shell.execute_reply.started":"2021-07-12T13:19:54.034857Z","shell.execute_reply":"2021-07-12T13:20:06.079344Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from memory_profiler import profile\ndef large_integer_list():\n    return [i for i in range(0,10000000)]\n\nsum(large_integer_list())","metadata":{"_uuid":"70588338-3424-4767-acf2-60677e8031d6","_cell_guid":"461f5028-d65c-4aef-bcfa-25260af7de3b","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:20:06.08181Z","iopub.execute_input":"2021-07-12T13:20:06.082145Z","iopub.status.idle":"2021-07-12T13:20:06.98458Z","shell.execute_reply.started":"2021-07-12T13:20:06.082108Z","shell.execute_reply":"2021-07-12T13:20:06.983323Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### メモリ使用状況確認","metadata":{}},{"cell_type":"code","source":"import sys\nprint(\"{}{:>25}{}{:>10}{}\".format('|','Variable Name','|','memory','|'))\nfor var_name in dir():\n    if not var_name.startswith(\"_\") and sys.getsizeof(eval(var_name)) > 10000:\n        print(\"{}{:>25}{}{:>10}{}\".format('|',var_name,'|',sys.getsizeof(eval(var_name)),'|'))","metadata":{"_uuid":"7061e51b-719e-4d36-9cf4-8ce160b371ea","_cell_guid":"176e120c-08b6-445b-b706-9dff3da570c8","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:20:06.986626Z","iopub.execute_input":"2021-07-12T13:20:06.987079Z","iopub.status.idle":"2021-07-12T13:20:07.981475Z","shell.execute_reply.started":"2021-07-12T13:20:06.987028Z","shell.execute_reply":"2021-07-12T13:20:07.980187Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X = tr[FECOLS+MEDCOLS].values\ny = tr[TGTCOLS].values\ncl = tr[\"playerId\"].values\ntr","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:20:07.982956Z","iopub.execute_input":"2021-07-12T13:20:07.983311Z","iopub.status.idle":"2021-07-12T13:20:10.749962Z","shell.execute_reply.started":"2021-07-12T13:20:07.983272Z","shell.execute_reply":"2021-07-12T13:20:10.748861Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"NFOLDS = 6\nskf = StratifiedKFold(n_splits=NFOLDS)\nfolds = skf.split(X, cl)\nfolds = list(folds)\n\nimport tensorflow as tf\nimport tensorflow.keras.layers as L\nimport tensorflow.keras.models as M\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\nfrom tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n\ntf.random.set_seed(777)","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:20:10.75146Z","iopub.execute_input":"2021-07-12T13:20:10.751799Z","iopub.status.idle":"2021-07-12T13:20:22.885542Z","shell.execute_reply.started":"2021-07-12T13:20:10.751763Z","shell.execute_reply":"2021-07-12T13:20:22.884312Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### モデルANN","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:94c7a4a8-9a74-41c8-a721-f2992af9869d.png)","metadata":{},"attachments":{"94c7a4a8-9a74-41c8-a721-f2992af9869d.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"ANNは、単純な計算単位（ノードとも呼ばれます）のセットです。\n高度に相互接続されています。 ANNはさまざまなアプリケーションに使用されてきました。\n統計的手法が伝統的に採用されている場所。それらはで使用されています。","metadata":{}},{"cell_type":"code","source":"def make_model(n_in):\n    inp = L.Input(name=\"inputs\", shape=(n_in,))\n    x = L.Dense(50, activation=\"relu\", name=\"d1\")(inp)\n    x = L.Dense(50, activation=\"relu\", name=\"d2\")(x)\n    preds = L.Dense(4, activation=\"linear\", name=\"preds\")(x)\n    \n    model = M.Model(inp, preds, name=\"ANN\")\n    model.compile(loss=\"mean_absolute_error\", optimizer=\"adam\")\n    return model","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:20:22.887173Z","iopub.execute_input":"2021-07-12T13:20:22.887563Z","iopub.status.idle":"2021-07-12T13:20:22.89507Z","shell.execute_reply.started":"2021-07-12T13:20:22.887524Z","shell.execute_reply":"2021-07-12T13:20:22.893823Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"net = make_model(X.shape[1])\nprint(net.summary())","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:20:22.896754Z","iopub.execute_input":"2021-07-12T13:20:22.897407Z","iopub.status.idle":"2021-07-12T13:20:23.250547Z","shell.execute_reply.started":"2021-07-12T13:20:22.897356Z","shell.execute_reply":"2021-07-12T13:20:23.249493Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"学習をやってみる前に最適な学習率の変化の計画を立てておくことは非常に困難です。\n最初は大きめの値でどんどん学習して、それではうまくいかなくなった段階で徐々に下げるということをやりたくなります。\n\nそして、 kerasにはそのためのコールバックの、ReduceLROnPlateau というのが用意されています。\n監視する評価値、何エポック改善しなかったら学習率を落とすか、その変化の割合、最小値などを指定すると、\n学習の進みに応じて調整してくれます。","metadata":{}},{"cell_type":"code","source":"oof = np.zeros(y.shape)\nnets = []\nfor idx in range(NFOLDS):\n    print(\"FOLD:\", idx)\n    tr_idx, val_idx = folds[idx]\n    ckpt = ModelCheckpoint(f\"w{idx}.h5\", monitor='val_loss', verbose=1, save_best_only=True,mode='min')\n    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,patience=3, min_lr=0.0005)\n    es = EarlyStopping(monitor='val_loss', patience=6)\n    reg = make_model(X.shape[1])\n    reg.fit(X[tr_idx], y[tr_idx], epochs=15, batch_size=35_000, validation_data=(X[val_idx], y[val_idx]),#epoch 10=>15\n            verbose=1, callbacks=[ckpt, reduce_lr, es])\n    reg.load_weights(f\"w{idx}.h5\")\n    oof[val_idx] = reg.predict(X[val_idx], batch_size=50_000, verbose=1)\n    nets.append(reg)\n    gc.collect()\n ","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:20:23.254322Z","iopub.execute_input":"2021-07-12T13:20:23.254689Z","iopub.status.idle":"2021-07-12T13:24:27.108857Z","shell.execute_reply.started":"2021-07-12T13:20:23.254645Z","shell.execute_reply":"2021-07-12T13:24:27.107323Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mae = mean_absolute_error(y, oof)\nmse = mean_squared_error(y, oof, squared=False)\nprint(\"mae:\", mae)\nprint(\"mse:\", mse)\n\n# Historical information to use in prediction time\nbound_dt = pd.to_datetime(\"2021-01-01\")\nLAST = tr.loc[tr.EvalDate>bound_dt].copy()\n\nLAST_MED_DF = MED_DF.loc[MED_DF.EvalYear==2021].copy()\nLAST_MED_DF.drop(\"EvalYear\", axis=1, inplace=True)\ndel tr\n\n#\"\"\"\nimport mlb\nFE = []; SUB = [];","metadata":{"_uuid":"58298437-91d6-432c-9235-ba4fe1461046","_cell_guid":"d538dcce-f275-4d5b-9097-cc263e1d0d38","collapsed":false,"papermill":{"duration":252.120183,"end_time":"2021-06-28T10:14:04.030716","exception":false,"start_time":"2021-06-28T10:09:51.910533","status":"completed"},"tags":[],"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2021-07-12T13:24:27.111545Z","iopub.execute_input":"2021-07-12T13:24:27.1119Z","iopub.status.idle":"2021-07-12T13:24:27.619315Z","shell.execute_reply.started":"2021-07-12T13:24:27.111865Z","shell.execute_reply":"2021-07-12T13:24:27.618085Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# envセクション","metadata":{}},{"cell_type":"code","source":"import copy\n\nenv = mlb.make_env() # initialize the environment\niter_test = env.iter_test() # iterator which loops over each date in test set","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:24:27.620953Z","iopub.execute_input":"2021-07-12T13:24:27.6213Z","iopub.status.idle":"2021-07-12T13:24:27.626524Z","shell.execute_reply.started":"2021-07-12T13:24:27.621265Z","shell.execute_reply":"2021-07-12T13:24:27.625461Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for (test_df, sample_prediction_df) in iter_test: # make predictions here\n    \n    sub = copy.deepcopy(sample_prediction_df.reset_index())\n    sample_prediction_df = copy.deepcopy(sample_prediction_df.reset_index(drop=True))\n    \n    # LGBM summit\n    # creat dataset\n    sample_prediction_df['playerId'] = sample_prediction_df['date_playerId']\\\n                                        .map(lambda x: int(x.split('_')[1]))\n    # Dealing with missing values\n    if test_df['rosters'].iloc[0] == test_df['rosters'].iloc[0]:\n        test_rosters = pd.DataFrame(eval(test_df['rosters'].iloc[0]))\n    else:\n        test_rosters = pd.DataFrame({'playerId': sample_prediction_df['playerId']})\n        for col in rosters.columns:\n            if col == 'playerId': continue\n            test_rosters[col] = np.nan\n            \n    if test_df['playerBoxScores'].iloc[0] == test_df['playerBoxScores'].iloc[0]:\n        test_scores = pd.DataFrame(eval(test_df['playerBoxScores'].iloc[0]))\n    else:\n        test_scores = pd.DataFrame({'playerId': sample_prediction_df['playerId']})\n        for col in scores.columns:\n            if col == 'playerId': continue\n            test_scores[col] = np.nan\n    test_scores = test_scores.groupby('playerId').sum().reset_index()\n    test = sample_prediction_df[['playerId']].copy()\n    test = test.merge(players[players_cols], on='playerId', how='left')\n    test = test.merge(test_rosters[rosters_cols], on='playerId', how='left')\n    test = test.merge(test_scores[scores_cols], on='playerId', how='left')\n    test = test.merge(player_target_stats, how='inner', left_on=[\"playerId\"],right_on=[\"playerId\"])\n    \n\n    test['label_playerId'] = test['playerId'].map(player2num)\n    test['label_primaryPositionName'] = test['primaryPositionName'].map(position2num)\n    test['label_teamId'] = test['teamId'].map(teamid2num)\n    test['label_status'] = test['status'].map(status2num)\n    \n    test_X = test[feature_cols]\n    # predict\n    pred1 = model1.predict(test_X)\n    test['target1'] = np.clip(pred1,0,100)\n    test_X = test[feature_cols2]\n\n    pred2 = model2.predict(test_X)\n    pred3 = model3.predict(test_X)\n    pred4 = model4.predict(test_X)\n    \n    # merge submission\n    sample_prediction_df['target1'] = np.clip(pred1, 0, 100)\n    sample_prediction_df['target2'] = np.clip(pred2, 0, 100)\n    sample_prediction_df['target3'] = np.clip(pred3, 0, 100)\n    sample_prediction_df['target4'] = np.clip(pred4, 0, 100)\n    sample_prediction_df = sample_prediction_df.fillna(0.)\n    del sample_prediction_df['playerId']\n    # TF summit\n    # Features computation at Evaluation Date\n    sub_fe, eval_dt = test_lag(sub)\n    sub_fe = sub_fe.merge(LAST_MED_DF, on=\"playerId\", how=\"left\")\n    sub_fe = sub_fe.fillna(0.)\n    \n    _preds = 0.\n    for reg in nets:\n        _preds += reg.predict(sub_fe[FECOLS + MEDCOLS]) / NFOLDS\n    sub_fe[TGTCOLS] = np.clip(_preds, 0, 100)\n    sub.drop([\"date\"]+TGTCOLS, axis=1, inplace=True)\n    sub = sub.merge(sub_fe[[\"playerId\"]+TGTCOLS], on=\"playerId\", how=\"left\")\n    sub.drop(\"playerId\", axis=1, inplace=True)\n    sub = sub.fillna(0.)\n    # Blending\n    blend = pd.concat(\n        [sub[['date_playerId']],\n        (0.1*sub.drop('date_playerId', axis=1) + 0.9*sample_prediction_df.drop('date_playerId', axis=1))], #0.1,0.9\n        axis=1\n    )\n    env.predict(blend)\n    # Update Available information\n    sub_fe[\"EvalDate\"] = eval_dt\n    #sub_fe.drop(MEDCOLS, axis=1, inplace=True)\n    LAST = LAST.append(sub_fe)\n    LAST = LAST.drop_duplicates(subset=[\"EvalDate\",\"playerId\"], keep=\"last\")","metadata":{"_uuid":"c3248f0b-ff1e-4f57-b87d-f011acf9a2ad","_cell_guid":"ba338cfe-322c-42a3-8172-ad2d5f10ffd4","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":9.788273,"end_time":"2021-06-28T10:14:14.431437","exception":false,"start_time":"2021-06-28T10:14:04.643164","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-07-12T13:24:27.62812Z","iopub.execute_input":"2021-07-12T13:24:27.628565Z","iopub.status.idle":"2021-07-12T13:24:37.98316Z","shell.execute_reply.started":"2021-07-12T13:24:27.628527Z","shell.execute_reply":"2021-07-12T13:24:37.981875Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This version of the API is not optimized and should not be used to estimate the runtime of your code on the hidden test set.<br>\nのメッセージはどうやら意味はあまりないようです。","metadata":{}},{"cell_type":"markdown","source":"### 分解","metadata":{}},{"cell_type":"code","source":"pd.concat(\n    [sub[['date_playerId']],\n    (sub.drop('date_playerId', axis=1) + sample_prediction_df.drop('date_playerId', axis=1)) / 2],\n    axis=1\n)","metadata":{"_uuid":"5f7b9da0-82e8-41b3-8249-f25aa7cfd3f6","_cell_guid":"d8b9bf4d-16bb-4eff-9786-236f7a3939f2","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":0.712316,"end_time":"2021-06-28T10:14:15.77725","exception":false,"start_time":"2021-06-28T10:14:15.064934","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-07-12T13:24:37.984915Z","iopub.execute_input":"2021-07-12T13:24:37.985395Z","iopub.status.idle":"2021-07-12T13:24:38.009038Z","shell.execute_reply.started":"2021-07-12T13:24:37.985341Z","shell.execute_reply":"2021-07-12T13:24:38.008133Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_prediction_df","metadata":{"_uuid":"d7c2f293-11f2-4b7d-b2db-87054fbe0475","_cell_guid":"0d151e89-8cac-48ea-bfa6-10f9b440d63a","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":0.639347,"end_time":"2021-06-28T10:14:17.032356","exception":false,"start_time":"2021-06-28T10:14:16.393009","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-07-12T13:24:38.0103Z","iopub.execute_input":"2021-07-12T13:24:38.010731Z","iopub.status.idle":"2021-07-12T13:24:38.03706Z","shell.execute_reply.started":"2021-07-12T13:24:38.010695Z","shell.execute_reply":"2021-07-12T13:24:38.036307Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"eval関数を使って結果を変数に格納したい場合には、\n\n変数 = eval(\"式\")","metadata":{}},{"cell_type":"code","source":" pd.DataFrame(eval(test_df['playerBoxScores'].iloc[0]))","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:24:38.03855Z","iopub.execute_input":"2021-07-12T13:24:38.038926Z","iopub.status.idle":"2021-07-12T13:24:38.290421Z","shell.execute_reply.started":"2021-07-12T13:24:38.03889Z","shell.execute_reply":"2021-07-12T13:24:38.289441Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"evalを使わないと以下のようになる。pandasに無理やりいれようとするとエラーになりました。","metadata":{}},{"cell_type":"code","source":"test_df['playerBoxScores'].iloc[0][:500]","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:24:38.291976Z","iopub.execute_input":"2021-07-12T13:24:38.292331Z","iopub.status.idle":"2021-07-12T13:24:38.298548Z","shell.execute_reply.started":"2021-07-12T13:24:38.292296Z","shell.execute_reply":"2021-07-12T13:24:38.297614Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pd.DataFrame(pred1)","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:24:38.299808Z","iopub.execute_input":"2021-07-12T13:24:38.30032Z","iopub.status.idle":"2021-07-12T13:24:38.3239Z","shell.execute_reply.started":"2021-07-12T13:24:38.300279Z","shell.execute_reply":"2021-07-12T13:24:38.322506Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"NumPy配列ndarrayを任意の最小値・最大値に収めるclip<br>\n1183行目の負値が0になっている。","metadata":{}},{"cell_type":"code","source":"pd.DataFrame(np.clip(pred1, 0, 100))","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:24:38.326445Z","iopub.execute_input":"2021-07-12T13:24:38.326968Z","iopub.status.idle":"2021-07-12T13:24:38.343266Z","shell.execute_reply.started":"2021-07-12T13:24:38.326911Z","shell.execute_reply":"2021-07-12T13:24:38.342102Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub_fe","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:24:38.34476Z","iopub.execute_input":"2021-07-12T13:24:38.345171Z","iopub.status.idle":"2021-07-12T13:24:38.392643Z","shell.execute_reply.started":"2021-07-12T13:24:38.345134Z","shell.execute_reply":"2021-07-12T13:24:38.391418Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"[sub[['date_playerId']],\n        (0.1*sub.drop('date_playerId', axis=1))]","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:24:38.394286Z","iopub.execute_input":"2021-07-12T13:24:38.394779Z","iopub.status.idle":"2021-07-12T13:24:38.411666Z","shell.execute_reply.started":"2021-07-12T13:24:38.394723Z","shell.execute_reply":"2021-07-12T13:24:38.410308Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"[(0.9*sample_prediction_df.drop('date_playerId', axis=1))]","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:24:38.41354Z","iopub.execute_input":"2021-07-12T13:24:38.414028Z","iopub.status.idle":"2021-07-12T13:24:38.436673Z","shell.execute_reply.started":"2021-07-12T13:24:38.413975Z","shell.execute_reply":"2021-07-12T13:24:38.435551Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### このconcatでの割合がポイントになってくる。\n### だいたい複数のモデルの結果を最後に統合するアンサンブルというのをやっています。\n### これだと２つのモデルの結果を、よさげなモデルの方の比率（下の例だと0.8倍）しています。","metadata":{}},{"cell_type":"code","source":"pd.concat(\n        [sub[['date_playerId']],\n        (0.2*sub.drop('date_playerId', axis=1) + 0.8*sample_prediction_df.drop('date_playerId', axis=1))],\n        axis=1)","metadata":{"execution":{"iopub.status.busy":"2021-07-12T13:30:05.068647Z","iopub.execute_input":"2021-07-12T13:30:05.069198Z","iopub.status.idle":"2021-07-12T13:30:05.100052Z","shell.execute_reply.started":"2021-07-12T13:30:05.069157Z","shell.execute_reply":"2021-07-12T13:30:05.098794Z"},"trusted":true},"execution_count":null,"outputs":[]}]}